load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse")

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """\
filegroup(
    name = "includes",
    srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
    name = "numpy_headers",
    hdrs = [":includes"],
    strip_include_prefix="site-packages/numpy/core/include/",
)
""",
    ),
}

pip_parse(

# TODO(b/304751256): Adjust the numpy pin to a single version, when ready
numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras

setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"
scipy==1.9.2;python_version=="3.11"
scipy==1.7.3;python_version<"3.11"
ml_dtypes>=0.2.0

    def render(self, content: Any) -> bytes:
        assert orjson is not None, "orjson must be installed to use ORJSONResponse"
        return orjson.dumps(
            content, option=orjson.OPT_NON_STR_KEYS | orjson.OPT_SERIALIZE_NUMPY

  }
  std::string value_string;
  if (!SummarizeValue(value_string).ok()) {
    value_string = "<error computing value>";
  }
  if (value_string.length() > 100) {
    // The default NumPy-style output can be distractingly long in error
    // messages.
    value_string = absl::StrCat(value_string.substr(0, 100), " [...]");
  }
  Status s;
  const char* device_name = DeviceName(&s);
  if (!s.ok()) {

      with:
        python-version: "3.9"
    - name: Install Python dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pylint==2.13.9 numpy wheel
    - name: Run PyLint on changed files
      run: |

#### *Try your first TensorFlow program*

```shell
$ python
```

```python
>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'
```

For more examples, see the
[TensorFlow tutorials](https://www.tensorflow.org/tutorials/).

## Contribution guidelines

    cryptography cycler Cython decorator google-api-python-client \
    google-auth google-auth-httplib2 grpcio httplib2 idna ipython-genutils \
    kiwisolver macholib matplotlib nose numpy packaging pandas pickleshare pip \
    prompt-toolkit protobuf psutil pyasn1 pyasn1-modules pycparser Pygments \
    pyparsing pyreadline python-dateutil pytz pywin32 requests rsa setuptools \

namex==0.0.7 \
    --hash=sha256:84ba65bc4d22bd909e3d26bf2ffb4b9529b608cb3f9a4336f776b04204ced69b \
    --hash=sha256:8a4f062945f405d77cb66b907f16aa2fd83681945e998be840eb6c4154d40108
    # via keras-nightly
numpy==1.23.5 ; python_version <= "3.11" \
    --hash=sha256:01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d \
    --hash=sha256:06005a2ef6014e9956c09ba07654f9837d9e26696a0470e42beedadb78c11b07 \

    [`tf.experimental.numpy`](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy),
    which is a NumPy-compatible API for writing TF programs. This module
    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is